I am Emmy Noether junior group leader at the Munich Center for Mathematical Philosophy (MCMP, LMU Munich). I am also member of the Young Center of the LMU Center for Advanced Studies, and associated member of the Machine Learning group of the Dutch national research institute for mathematics and computer science.

I am interested in applying the mathematical field of machine learning theory to philosophical questions around machine learning and artificial intelligence. My Emmy Noether project From Bias to Knowledge: The Epistemology of Machine Learning is concerned with the fundamental notion of inductive bias—the assumptions that allow a learning algorithm to learn. This project builds on my earlier German Science Foundation-funded project on the The Epistemology of Statistical Learning Theory.

I think climate change is a thing, and I don't see why we all couldn't try to make an effort. So I avoid flying, and aim to only travel to places I can reach by train.

Contact me at tom.sterkenburglmu.de.

Current affairs

May 2025. I speak at the First Workshop on Machine Learning under Weakly Structured Information at the LMU Department of Statistics, and at the Workshop Generalization and Overfitting at the High-Performance Computing Center Stuttgart.

Apr 2024. In the 2024 summer semester I organize, together with Timo Freiesleben, a new round of the MCMP reading group on philosophy of machine learning.

Apr 2024. I give an invited talk in the Séminaire général de l'IHPST, Paris 1 Panthéon-Sorbonne University, and a submitted talk at the ML, Explain Yourself! conference in Utrecht.

Mar 2024. I speak at the MCMP interdisciplinary Workshop on Modelling Complex Systems in climate science.

Feb 2024. I give an invited talk at at the Epistemological Issues of Machine Learning in Science workshop in Dortmund.

Jan 2024. I give an invited talk in the Computational Health Seminar at Helmholtz Munich. I serve as program committee member for ACM FAccT'24.

Formerly current affairs...


In my DFG-funded Eigene Stelle project The Epistemology of Statistical Learning Theory (2020-2023), I explored the epistemological import of statistical learning theory, the standard theoretical framework for modern machine learning methods. I also proved new results in the recently proposed setting of computable PAC learning.

As a postdoctoral fellow at the MCMP (2017-2020), I investigated the meta-inductive justification of induction that is based on the machine learning theory of online prediction. I further worked on a Bayesian confirmation theory that can deal with newly formulated hypotheses, that also drew from results in online prediction.

In my PhD project (2013-2018, cum laude), at the CWI and the Faculty of Philosophy of the University of Groningen, I investigated the theory of universal prediction stemming from algorithmic information theory (Kolmogorov complexity). My PhD dissertation on Universal Prediction won me the Wolfgang Stegmüller Award.

I hold a MSc in Logic (Institute for Logic, Language and Computation, University of Amsterdam, cum laude), a MSc in History and Philosophy of Science (Descartes Centre, Utrecht University, cum laude), and a BSc in Artificial Intelligence (VU University Amsterdam, cum laude). I have never managed to obtain my driver's license, but I hope to attain Deutsche Bahn Statuslevel Gold Platin soon.

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Last update: 04/2024.